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These transactions publish research in computer-based methods of computational collective intelligence (CCI) and their applications in a wide range of fields such as performance optimization in IoT, big data, reliability, privacy, security, service selection, QoS and machine learning. This 36th issue contains 7 selected papers which present new findings and innovative methodologies as well as discuss issues and challenges in the field of collective intelligence from big data and networking paradigms while addressing security, privacy, reliability and optimality to achieve QoS to the benefit of final users This is an open access book.
These transactions publish research in computer-based methods of computational collective intelligence (CCI) and their applications in a wide range of fields such as performance optimization in IoT, big data, reliability, privacy, security, service selection, QoS and machine learning. This 37th issue contains 9 selected papers which present new findings and innovative methodologies as well as discuss issues and challenges in the field of collective intelligence from big data and networking paradigms while addressing security, privacy, reliability and optimality to achieve QoS to the benefit of final users.
Written by leading scholars from various disciplines, this book presents current research on topics such as public choice, game theory, and political economy. It features contributions on fundamental, methodological, and empirical issues around the concepts of power and responsibility that strive to bridge the gap between different disciplinary approaches. The contributions fall into roughly four sub-disciplines: voting and voting power, public economics and politics, economics and philosophy, as well as labor economics. On the occasion of his 75th birthday, this book is written in honor of Manfred J. Holler, an economist by training and profession whose work as a guiding light has helped advance our understanding of the interdisciplinary connections of concepts of power and responsibility. He has written many articles and books on game theory, and worked extensively on questions of labor economics, politics, and philosophy.
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A gentle introduction to genetic algorithms. Genetic algorithms revisited: mathematical foundations. Computer implementation of a genetic algorithm. Some applications of genetic algorithms. Advanced operators and techniques in genetic search. Introduction to genetics-based machine learning. Applications of genetics-based machine learning. A look back, a glance ahead. A review of combinatorics and elementary probability. Pascal with random number generation for fortran, basic, and cobol programmers. A simple genetic algorithm (SGA) in pascal. A simple classifier system(SCS) in pascal. Partition coefficient transforms for problem-coding analysis.
Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures.
The latest edition of this classic is updated with new problem sets and material The Second Edition of this fundamental textbook maintains the book's tradition of clear, thought-provoking instruction. Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory. All the essential topics in information theory are covered in detail, including entropy, data compression, channel capacity, rate distortion, network information theory, and hypothesis testing. The authors provide readers with a solid understanding of the underlying theory and applications. Problem sets and a telegraphic summary at the end of each chapter further assist readers. The historical notes that follow each chapter recap the main points. The Second Edition features: * Chapters reorganized to improve teaching * 200 new problems * New material on source coding, portfolio theory, and feedback capacity * Updated references Now current and enhanced, the Second Edition of Elements of Information Theory remains the ideal textbook for upper-level undergraduate and graduate courses in electrical engineering, statistics, and telecommunications.
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.